Jon Paul Janet

Publications

  1. Representations and Strategies for Transferable Machine Learning Improve Model Performance in Chemical Discovery (2022)
  2. Navigating Transition-Metal Chemical Space: Artificial Intelligence for First-Principles Design (2021)
  3. Understanding the diversity of the metal-organic framework ecosystem (2020)
  4. Machine Learning in Chemistry (2020)
  5. Accurate Multiobjective Design in a Space of Millions of Transition Metal Complexes with Neural-Network-Driven Efficient Global Optimization (2020)
  6. Seeing Is Believing: Experimental Spin States from Machine Learning Model Structure Predictions (2020)
  7. Enumeration of de novo inorganic complexes for chemical discovery and machine learning (2020)
  8. Machine Learning Accelerates the Discovery of Design Rules and Exceptions in Stable Metal–Oxo Intermediate Formation (2019)
  9. Designing in the Face of Uncertainty: Exploiting Electronic Structure and Machine Learning Models for Discovery in Inorganic Chemistry (2019)
  10. Learning from Failure: Predicting Electronic Structure Calculation Outcomes with Machine Learning Models (2019)
  11. A quantitative uncertainty metric controls error in neural network-driven chemical discovery (2019)
  12. Strategies and Software for Machine Learning Accelerated Discovery in Transition Metal Chemistry (2018)
  13. Accelerating Chemical Discovery with Machine Learning: Simulated Evolution of Spin Crossover Complexes with an Artificial Neural Network (2018)
  14. Communication: Recovering the flat-plane condition in electronic structure theory at semi-local DFT cost (2017)
  15. Resolving Transition Metal Chemical Space: Feature Selection for Machine Learning and Structure–Property Relationships (2017)
  16. Leveraging Cheminformatics Strategies for Inorganic Discovery: Application to Redox Potential Design (2017)
  17. Density functional theory for modelling large molecular adsorbate–surface interactions: a mini-review and worked example (2017)
  18. Predicting electronic structure properties of transition metal complexes with neural networks (2017)